Sometimes, we want to make strike-zone plots of many pitches at once (for example, see Jim’s great post on visualizing Cliff Lee’s pitches). When dealing with many pitches, it’s easy to make some points indistinguishable from one another. This is a problem, since it can lead to a mis-representation of the true density of pitches in certain locations. In the data visualization community, this problem is generally known as “over-plotting”. Stephen Few has a great overview of things you can do to avoid over-plotting. I will apply a couple of these techniques to strike-zone plots with help from pitchRx and ggplot2.

Last time I posted, we focused on querying data from our PITCHf/x database. Let’s use this approach to grab data on every pitch thrown by Clayton Kershaw’s during 2013 season.

library(dplyr)
library(pitchRx)
setwd("~/pitchfx") # My directory that contains a SQLITE DB with PITCHf/x 
db <- src_sqlite("pitchRx.sqlite3")
atbats <- tbl(db, "atbat") %.%
  filter(date >= '2013_01_01' & date <= '2014_01_01') %.%
  filter(pitcher_name == 'Clayton Kershaw')
pitches <- tbl(db, "pitch")  
kershaw <- collect(inner_join(pitches, atbats, by = c("num", "gameday_link")))

Just to get an idea what we are working with, let’s count the number of different pitch types thrown by Kershaw broken down by batter stance:

kershaw %.% 
  group_by(pitch_type, stand) %.%
  summarise(count = n()) %.%
  arrange(desc(count))

## Source: local data frame [11 x 3]
## Groups: pitch_type
## 
##    pitch_type stand count
## 1          FF     R  1831
## 2          SL     R   770
## 3          FF     L   594
## 4          CU     R   405
## 5          SL     L   211
## 6          NA     R   109
## 7          CH     R    98
## 8          CU     L    97
## 9          NA     L    22
## 10         IN     R     7
## 11         CH     L     2

The pitch type abbreviations ‘FF’, ‘SL’, ‘CU’, ‘CH’, and ‘IN’ stand for ‘four-seam fastball’, ‘slider’, ‘curveball’, ‘change-up’, and ‘intentional walk’ respectively. The intentional walks won’t be very interesting from a visual standpoint, so let’s get rid of them:

kershaw <- filter(kershaw, pitch_type != "IN")

The strikeFX function from the pitchRx package was created to give us a quick yet flexible way to visualize PITCHf/x data. Even if you’re not versed in ggplot2, it’s easy to make strike-zone plots.

# strikeFX knows to use the 'px' and 'pz' columns for 'x' and 'y'
strikeFX(kershaw)

plot of chunk strikeFX

The two black rectangles correspond to left-handed and right-handed strikezones created using the approach Mike Fast suggests in this post. Since the strike-zone depends on the batters height, strikeFX has an option to adjust pitch locations to account for the “averaged” strike-zones on the plot. strikeFX uses a variety of defaults (such as coloring points by the pitch type) that can be altered. In addition to altering defaults, arguments to strikeFX can also add elements such as contour lines to the graphic (see the documentation for other arguments). Note that the type variable contains abbreviations ‘B’, ‘S’, and ‘X’ which stands for ‘Ball’, ‘Strike’, and ‘Hit in play’.

strikeFX(kershaw, color = "type", point.alpha = 0.5, adjust = TRUE, contour = TRUE)

If you’re familiar with ggplot2, we can take advantage of it’s arithmetic approach to modify graphical elements, add complexities, and customize the appearance. To demonstrate, I’ll take essentially the same plot, but place pitches thrown to left-handed and right-handed batters into separate plots (facet_grid), move the location of the legend (theme), fix the ratio between the axes and the plot presentation (coord_equal), and change the background from gray to white (theme_bw).

strikeFX(kershaw, color = "type", point.alpha = 0.2,
         adjust = TRUE, contour = TRUE) + facet_grid(. ~ stand) + 
  theme(legend.position = "right", legend.direction = "vertical") +
  coord_equal() + theme_bw()

plot of chunk type1

Now it’s clear that the density estimate (that is, the contour lines) in the second plot was heavily influenced by pitches thrown to right-handed batters. We can gain further insight by simply introducing pitch_type to facet_grid.

strikeFX(kershaw, color = "type", point.alpha = 0.1,
         adjust = TRUE, contour = TRUE) + facet_grid(pitch_type ~ stand) + 
  theme(legend.position = "right", legend.direction = "vertical") +
  coord_equal() + theme_bw()

plot of chunk type2

Now it’s clear that a much lower proportion of strikes occur outside of the strike-zone for four-seamers (compared to the other pitch types). Also, the location of highest density is much higher in the strike-zone for four-seamers (compared to the other pitch types). This shouldn’t be that suprising, but reassuring that the data matches intuition.

In addition to using a categorical variable for color assignment, we can also use a numerical variable and strikeFX will automatically know to use a one-way color scale:

strikeFX(kershaw, color = "end_speed", point.alpha = 0.1, 
         adjust = TRUE, contour = TRUE) + facet_grid(pitch_type ~ stand) + 
  theme(legend.position = "right", legend.direction = "vertical") +
  coord_equal() + theme_bw()

plot of chunk end_speed

From this plot, it’s fairly obvious that end_speed is a good indicator of pitch_type, except that it doesn’t provide a great distinction between change-ups (CH) and sliders (SL).

For more demonstrations of the capabilities of strikeFX, check out the pitchRx introduction page.